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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "MLPModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_mlp.MLPConfig",
+    "AutoModel": "modeling_mlp.MLPModel"
+  },
+  "hidden_act": "relu",
+  "hidden_size": [
+    256,
+    256
+  ],
+  "initializer_range": 0.02,
+  "input_size": 64,
+  "model_type": "mlp",
+  "num_hidden_layers": 2,
+  "output_size": 2,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2"
+}

configuration_mlp.py

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+from transformers import PretrainedConfig
+
+
+class MLPConfig(PretrainedConfig):
+    model_type = "mlp"
+
+    def __init__(
+        self,
+        num_hidden_layers: int = 2,
+        input_size: int = 64,
+        hidden_size: list[int] = [256, 256],
+        output_size: int = 2,
+        hidden_act: str = "relu",
+        initializer_range: float = 0.02,
+        **kwargs
+    ):
+        if len(hidden_size) != num_hidden_layers:
+            raise ValueError("num_hidden_layers should equal to len(hidden_size)")
+        
+        self.num_hidden_layers = num_hidden_layers
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.output_size = output_size
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        super().__init__(**kwargs)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1d8e72a0782bf6286404c5e4f88811239c4188bdae687ac45c549b99123db29c
+size 332296

modeling_mlp.py

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+import torch
+from torch import nn
+from typing import Optional
+from dataclasses import dataclass
+from transformers import PreTrainedModel
+from .configuration_mlp import MLPConfig
+from transformers.utils import ModelOutput
+from transformers.activations import ACT2FN
+
+
+@dataclass
+class MLPOutput(ModelOutput):
+    loss: Optional[torch.FloatTensor] = None
+    logits: Optional[torch.FloatTensor] = None
+
+
+class MLPPreTrainedModel(PreTrainedModel):
+    config_class = MLPConfig
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+
+
+class MLPModel(MLPPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.act_fn = ACT2FN[config.hidden_act]
+        iho = [config.input_size, *config.hidden_size, config.output_size]
+        self.linears = nn.ModuleList([
+            nn.Linear(iho[i], iho[i+1])
+            for i in range(config.num_hidden_layers + 1)
+        ])
+        self.loss_fn = nn.CrossEntropyLoss(reduce="mean")
+        # Initialize weights and apply final processing
+        self.post_init()
+    
+    def forward(self, inputs, labels=None):
+        for i in range(len(self.linears) - 1):
+            inputs = self.act_fn(self.linears[i](inputs))
+        logits = self.linears[-1](inputs)
+
+        loss = None
+        if labels is None:
+            return ModelOutput(loss=loss, logits=logits)
+        else:
+            loss = self.loss_fn(logits, labels)
+        return ModelOutput(loss=loss, logits=logits)